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Industry Keynote Talk by Ralf-Peter Schaefer, Head of Traffic Product Unit, TomTom, Germany at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management.
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Big (Traffic) Data
Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management
Ralf-Peter SchäferFellow & VP Traffic and Travel Information Product Unit
ralf-peter.schaefer@tomtom.com
1Copyrights: TomTom Internal BV 2014
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A B
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Huge investment and maintenance costs to detect traffic informationTypically every 2 km a loop required to get precise real-time traffic infos
Can we do better?
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Change
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Facebook Social Activity Graph (friend interactions)
Traffic Community of 350M+ connected users
• 9 trillion anonymous speed measurements (9.000.000.000.000)
• 8 billion speed measurements per day
(6.000.000.000)
• 22 trillion driving seconds
(22.000.000.000.000)
• Speed estimation via map matching and data analytics
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IQ Routes
GPS PROBE DATA
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Time-Space Characteristics
t
x
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Local detection (loops)
Moving Detection Floating Car
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Probe vehicle (e.g. GPS)
From Modeling to Measuring
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Classical tools for observing traffic flow: Simulation and Data from Loop-Detectors
Simulated elementary traffic jam patterns:
Interpolated and smoothed data from loop detectors:
From Modeling to Measuring
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Direct speeds observations with GPS probe data
• GPS data allows traffic observation everywhere
• Independent from stationary devices
• Sampling rate sufficient for real-time traffic information
TomTom Congestion Index Europe (Q3 2013)Traveltime delays compared to free flow situation at night hours
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http://www.tomtom.com/congestionindex
Speeds Over Time (City, e.g. Berlin)
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Sun Mon Tue Wed Thu Fri Sat
speed
Speed Frequencies Weekdays (City e.g. Berlin)
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6 km/h 43 km/h
num
ber
of
pro
bes
Speed Probe Data (Freeway, e.g. A9 south of Berlin)
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Speeds Over Time (Freeway , e.g. A9 south of Berlin)
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Sun Mon Tue Wed Thu Fri Sat
speed
ORIGIN-DESTINATION ZONE ANALYSIS
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Data collection of origin-destination data is difficult
Current techniques include
- Stop cars: road side interviews
- Get address from license plate and send survey
- Telephone interview
- Panel fills in a diary of their movements
- Point to point tracking: license plates (full) or bluetooth (sample)
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Selected link: links to links Selected link: zones to zones
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OD MatrixRoute choice
Detailed junction analysis per path
Speed measurements over time
Speed measurements grouped by day of the week
Speed frequency distribution, free flow estimate
Distribution of Travel time delta
Junction Stop Characteristics
Number of average stops per traversal
Average stop time per traversal
Data fusion Send to users
GPS Probe Data
GSM Probe Data
Journalistic info
Historic Traffic
Map Data
Various input sources
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REAL-TIME TRAFFIC INFO FROM USERS TO USERS
Probe Data Example of Traffic IncidentGPS and GSM input sources and incident output message
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• Example from Dec 13, 2011 • Near Stuttgart, Germany
• GPS data from floating cars• Speed data matched to road elements
• GSM data from mobile phone calls • Sophisticated algorithm
• After fusion and incident detection• Live incident output to PND
JAM TAIL WARNINGSDetection of jam tails for a safety warning in the navigation unit
• Over 35% of drivers have admitted to experiencing an accident caused by sudden or unexpected traffic holdups
• Jam ahead warning messages in traffic output can be used to create these safety messages with great accuracy
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Real-time road speed data
Enable traffic information and traffic management
Measured speed on each road segment
On all important roads
Without the need of road-side equipment
By using Floating Car Data
Updated every minute
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TOMTOM TRAFFICTraffic Flow
Flow conditions (speed) on all roads
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The TomTom Traffic Manifesto
If 10% of the road drivers use HD Traffic guided navigation in conurbations there is a
1. Individual journey time reduction for informed users by up to 15%
2. A collective journey time reduction for ALL by up to 5%
http://www.tomtom.com/trafficmanifesto
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10% 10%
How to estimate the journey time reduction claims in the TomTom Manifesto?
Use of traffic modelling and simulation in a simplified road networkAssume a share of equipped navigation users (e.g. traffic guided drivers) Assume high acceptance rate for detour choices when approaching a traffic jam!Results from simulation below for medium and high traffic flow
Source: F. Leurent, T. Nguyen, TRB 2010.
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Dynamic Navigation for personal and collective benefits24 Hour Time Lapse – NYC
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5 day historical– NYC
Monday
31 minutes
Tuesday
16 minutes
Wednesday
9 minutes
Thursday
18 minutes
Friday
4 minutes
Time Saved:1 Hour, 18 Minutes
The (future) Traffic Management ChallengeLoad balanced vs unbalanced routing system using dynamic route guidance
vs.
Educated Guess – Probe Data Source?
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Educated Guess 2nd – Probe Data Source?
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Thank Youralf-peter.schaefer@tomtom.com
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